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To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

To report on the translation procedures and psychometric properties of the DISCERN tool in Brazilian Portuguese. Three people translated the DISCERN from English into Brazilian Portuguese. A committee of experts and community representatives evaluated the quality of the 3 versions in 2 online voting rounds. Two native speakers back-translated the questionnaire into English. We compared these versions to the original DISCERN and made small adjustments. The final Brazilian Portuguese version of DISCERN was tested twice by journalism students to evaluate the quality of a text about smoking cessation treatments. We evaluated participants' health literacy with the Short Assessment of Health Literacy for Portuguese-Speaking Adults (SAHL-PA) tool, assessed the internal consistency of the translated questionnaire with the Cronbach test, and measured its reproducibility with the intraclass correlation coefficient (ICC). We then investigated the relationship between DISCERN and SAHL-PA scores and demographic variables. The participants (n = 126) had no difficulty in using the questionnaire. Cronbach's alpha was 0.865 (95% confidence interval [CI], 0.826-0.898), and the ICC between the 2 evaluations was 0.845 (CI 0.717-0.912). The mean health literacy of the participants was adequate. There was no correlation between the DISCERN score and the SAHL-PA score, age, or sex (P >.05). The Brazilian Portuguese version of the DISCERN questionnaire has excellent internal consistency and good reproducibility. The evaluators' ages, sex, and health literacy did not interfere with the score resulting from the evaluation of the quality of the text. [ABSTRACT FROM AUTHOR]

Studies on the transcriptional control of gene expression are crucial to understand changes in organism's physiological or cellular conditions. To obtain reliable data on mRNA amounts and the estimation of gene expression levels, it is crucial to normalize the target gene with one or more internal reference gene(s). However, the use of constitutive genes as reference genes is controversial, as their expression patterns are sometimes more complex than previously thought. In various arthropod vectors, including ticks, several constitutive genes have been identified by studying gene expression in different tissues and life stages. The cattle tick Rhipicephalus microplus is a major vector for several pathogens and is widely distributed in tropical and subtropical regions globally. Tick developmental physiology is an essential aspect of research, particularly embryogenesis, where many important developmental events occur, thus the identification of stable reference genes is essential for the interpretation of reliable gene expression data. This study aimed to identify and select R. microplus housekeeping genes and evaluate their stability during embryogenesis. Reference genes used as internal control in molecular assays were selected based on previous studies. These genes were screened by quantitative PCR (qPCR) and tested for gene expression stability during embryogenesis. Results demonstrated that the relative stability of reference genes varied at different time points during the embryogenesis. The GeNorm tool showed that elongation factor 1α (Elf1a) and ribosomal protein L4 (Rpl4) were the most stable genes, while H3 histone family 3A (Hist3A) and ribosomal protein S18 (RpS18) were the least stable. The NormFinder tool showed that Rpl4 was the most stable gene, while the ranking of Elf1a was intermediate in all tested conditions. The BestKeeper tool showed that Rpl4 and cyclophilin A (CycA) were the more and less stable genes, respectively. These data collectively demonstrate that Rpl4, Elf1a , and GAPDH are suitable internal controls for normalizing qPCR during R. microplus embryogenesis. These genes were consistently identified as the most stable in various analysis methods employed in this study. Thus, findings presented in this study offer valuable information for the study of gene expression during embryogenesis in R. microplus. [ABSTRACT FROM AUTHOR]

Acaricide resistance is a global problem that has impacts worldwide. Tick populations with broad resistance to all commercially available acaricides have been reported. Since resistance selection in ticks and their role in pathogen transmission to animals and humans result in important economic and public health burden, it is essential to develop new strategies for their control (i.e., novel chemical compounds, vaccines, biological control). The synganglion is the tick central nervous system and it is responsible for synthesizing and releasing signaling molecules with different physiological functions. Synganglion proteins are the targets of the majority of available acaricides. In this review we provide an overview of the mode-of-action and resistance mechanisms against neurotoxic acaricides in ticks, as well as putative target sites in synganglion, as a supporting tool to identify new target proteins and to develop new strategies for tick control. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: The claudin-low molecular subtype of breast cancer includes triple negative invasive carcinomas, with a high frequency of metaplastic and medullary features. The aim of this study was to evaluate the immunohistochemistry expression of claudins in a series of metaplastic breast carcinomas. We also assessed other claudin-low features, such as the cancer stem cell-like and epithelial-to-mesenchymal transition phenotypes. Results: The majority of the cases showed weak or negative staining for membrane claudins expression. We found 76.9% (10/13) low expressing cases for claudin-1, 84.6% (11/13) for claudin-3 and claudin-4, and 92.3% (12/13) for claudin-7. Regarding the cancer stem cell marker ALDH1, 30.8% (4/13) showed positive staining. We also showed that the majority of the cases presented a CD44+CD24−/low phenotype, positivity for vimentin and lack of E-cadherin expression. Interestingly, these claudin-low molecular features were specific of the mesenchymal component of metaplastic breast carcinomas, since its frequency was very low in other breast cancer molecular subtypes, as luminal, HER2-overexpressing and non-metaplastic triple negative tumors. Conclusions: The negative/low expression of claudins and E-cadherin, high levels of vimentin, and the breast cancer stem cell phenotype suggests that metaplastic breast carcinomas have similar features to the ones included in the claudin-low molecular subtype, specially their mesenchymal components. [Copyright &y& Elsevier]

Background: We previously identified dermicidin (DCD), which encodes a growth and survival factor, as a gene amplified and overexpressed in a subset of breast tumors. Patients with DCD-positive breast cancer have worse prognostic features. We therefore searched for specific molecular signatures in DCD-positive breast carcinomas from patients and representative cell lines. Methods: DCD expression was evaluated by qRT-PCR, immunohistochemical and immunoblot assays in normal and neoplastic tissues and cell lines. To investigate the role of DCD in breast tumorigenesis, we analyzed the consequences of its downregulation in human breast cancer cell lines using three specific shRNA lentiviral vectors. Genes up- and down-regulated by DCD were identified using Affymetrix microarray and analyzed by MetaCore Platform. Results: We identified DCD splice variant (DCD-SV) that is co-expressed with DCD in primary invasive breast carcinomas and in other tissue types and cell lines. DCD expression in breast tumors from patients with clinical follow up data correlated with high histological grade, HER2 amplification and luminal subtype. We found that loss of DCD expression led to reduced cell proliferation, resistance to apoptosis, and suppressed tumorigenesis in immunodeficient mice. Network analysis of gene expression data revealed perturbed ERBB signaling following DCD shRNA expression including changes in the expression of ERBB receptors and their ligands. Conclusions: These findings imply that DCD promotes breast tumorigenesis via modulation of ERBB signaling pathways. As ERBB signaling is also important for neural survival, HER2+ breast tumors may highjack DCD’s neural survival-promoting functions to promote tumorigenesis. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1022-6) contains supplementary material, which is available to authorized users.

Background: Scientific publications have been growing exponentially, contributing to an oversaturated information environment. Quantifying a research output's impact and reach cannot be solely measured by traditional metrics like citation counts as these have a lag time and are largely focused on an academic audience. There is increasing recognition to consider 'alternative metrics' or altmetrics to measure more immediate and broader impacts of research. Better understanding of altmetrics can help researchers better navigate evolving information environments and changing appetites for different types of research., Objectives: Our study aims to: 1) analyse the amount and medium of Altmetric coverage of health research produced by Irish organisations (2017 - 2023), identifying changes over time and 2) investigate differences in the amount of coverage between clinical areas (e.g., nutrition vs. neurology)., Methods: Using Altmetric institutional access, we will gather data on research outputs published 1 January 2017 through 31 December 2023 from active Irish organisations with Research Organisation Registry (ROR) IDs. Outputs will be deduplicated and stratified by their Australian and New Zealand Standard Research Classification relating to ≥1 field of health research: Biological Sciences, Biomedical and Clinical Sciences, Chemical Sciences, Health Sciences, and Psychology. We will clean data using R and perform descriptive analyses, establishing counts and frequencies of coverage by clinical area and medium (e.g., traditional news, X, etc.); data will be plotted on a yearly and quarterly basis where appropriate., Results and Conclusions: Improved understanding of one's information environment can help researchers better navigate their local landscapes and identify pathways for more effective communication to the public. All R code will be made available open-source, allowing researchers to adapt it to evaluate their local landscapes., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Sharp MK et al.)

Background: When research evidence is limited, inconsistent, or absent, healthcare decisions and policies need to be based on consensus amongst interested stakeholders. In these processes, the knowledge, experience, and expertise of health professionals, researchers, policymakers, and the public are systematically collected and synthesised to reach agreed clinical recommendations and/or priorities. However, despite the influence of consensus exercises, the methods used to achieve agreement are often poorly reported. The ACCORD (ACcurate COnsensus Reporting Document) guideline was developed to help report any consensus methods used in biomedical research, regardless of the health field, techniques used, or application. This explanatory document facilitates the use of the ACCORD checklist., Methods and Findings: This paper was built collaboratively based on classic and contemporary literature on consensus methods and publications reporting their use. For each ACCORD checklist item, this explanation and elaboration document unpacks the pieces of information that should be reported and provides a rationale on why it is essential to describe them in detail. Furthermore, this document offers a glossary of terms used in consensus exercises to clarify the meaning of common terms used across consensus methods, to promote uniformity, and to support understanding for consumers who read consensus statements, position statements, or clinical practice guidelines. The items are followed by examples of reporting items from the ACCORD guideline, in text, tables and figures., Conclusions: The ACCORD materials - including the reporting guideline and this explanation and elaboration document - can be used by anyone reporting a consensus exercise used in the context of health research. As a reporting guideline, ACCORD helps researchers to be transparent about the materials, resources (both human and financial), and procedures used in their investigations so readers can judge the trustworthiness and applicability of their results/recommendations., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: PL is a member of the UK EQUATOR Centre, based in the University of Oxford; EQUATOR promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. CCW is an employee, Director, and shareholder of Oxford PharmaGenesis Ltd., a Director of Oxford Health Policy Forum CIC, a Trustee of the Friends of the National Library of Medicine, and an Associate Fellow of Green Templeton College, University of Oxford. DT is co–editor-in-chief of the Journal of Clinical Epidemiology and chairs the Scientific Advisory Committee for the Centre for Biomedical Transparency. WG is an employee of Bristol Myers Squibb. NH is an employee of OPEN Health Communications. KG is an employee of AbbVie. KW and AC are employees of Oxford PharmaGenesis Ltd. AP, PB and EJvZ report no conflicts of interest. At the outset of the work, WG was an employee of Ipsen and NH was an employee of Ogilvy Health UK., (Copyright: © 2024 Logullo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: In biomedical research, it is often desirable to seek consensus among individuals who have differing perspectives and experience. This is important when evidence is emerging, inconsistent, limited, or absent. Even when research evidence is abundant, clinical recommendations, policy decisions, and priority-setting may still require agreement from multiple, sometimes ideologically opposed parties. Despite their prominence and influence on key decisions, consensus methods are often poorly reported. Our aim was to develop the first reporting guideline dedicated to and applicable to all consensus methods used in biomedical research regardless of the objective of the consensus process, called ACCORD (ACcurate COnsensus Reporting Document)., Methods and Findings: We followed methodology recommended by the EQUATOR Network for the development of reporting guidelines: a systematic review was followed by a Delphi process and meetings to finalize the ACCORD checklist. The preliminary checklist was drawn from the systematic review of existing literature on the quality of reporting of consensus methods and suggestions from the Steering Committee. A Delphi panel (n = 72) was recruited with representation from 6 continents and a broad range of experience, including clinical, research, policy, and patient perspectives. The 3 rounds of the Delphi process were completed by 58, 54, and 51 panelists. The preliminary checklist of 56 items was refined to a final checklist of 35 items relating to the article title (n = 1), introduction (n = 3), methods (n = 21), results (n = 5), discussion (n = 2), and other information (n = 3)., Conclusions: The ACCORD checklist is the first reporting guideline applicable to all consensus-based studies. It will support authors in writing accurate, detailed manuscripts, thereby improving the completeness and transparency of reporting and providing readers with clarity regarding the methods used to reach agreement. Furthermore, the checklist will make the rigor of the consensus methods used to guide the recommendations clear for readers. Reporting consensus studies with greater clarity and transparency may enhance trust in the recommendations made by consensus panels., Competing Interests: PL is a member of the UK EQUATOR Centre, based in the University of Oxford; EQUATOR promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. WTG is an employee of Bristol Myers Squibb. KG is an employee and shareholder of AbbVie. APH, in the past 5 years, has worked with Reckitt Benckiser for the development of the definitions and management of gastro-oesophageal reflux disease. CCW is an employee, Director, and shareholder of Oxford PharmaGenesis Ltd, a Director of Oxford Health Policy Forum CIC, a Trustee of the Friends of the National Library of Medicine, and an Associate Fellow of Green Templeton College, University of Oxford. NH is an employee of OPEN Health Communications. ELH is an employee of Camino Communications. DT is co–editor-in-chief of the Journal of Clinical Epidemiology and chairs the Scientific Advisory Committee for the Centre for Biomedical Transparency. AP, PB and EJvZ report no conflicts of interest. At the outset of the work, NH was an employee of Ogilvy Health UK, WTG was an employee of Ipsen, and ELH was an employee of OPEN Health Communications at the time of manuscript development., (Copyright: © 2024 Gattrell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant., Methods: In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively., Results: The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches., Conclusion: The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications., Advances in Knowledge: We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models' outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines., (© 2023 The Authors. Published by the British Institute of Radiology.)

Objective: To identify evidence on the reporting quality of consensus methodology and to select potential checklist items for the ACcurate COnsensus Reporting Document (ACCORD) project to develop a consensus reporting guideline., Design: Systematic review., Data Sources: Embase, MEDLINE, Web of Science, PubMed, Cochrane Library, Emcare, Academic Search Premier and PsycINFO from inception until 7 January 2022., Eligibility Criteria: Studies, reviews and published guidance addressing the reporting quality of consensus methodology for improvement of health outcomes in biomedicine or clinical practice. Reports of studies using or describing consensus methods but not commenting on their reporting quality were excluded. No language restrictions were applied., Data Extraction and Synthesis: Screening and data extraction of eligible studies were carried out independently by two authors. Reporting quality items addressed by the studies were synthesised narratively., Results: Eighteen studies were included: five systematic reviews, four narrative reviews, three research papers, three conference abstracts, two research guidance papers and one protocol. The majority of studies indicated that the quality of reporting of consensus methodology could be improved. Commonly addressed items were: consensus panel composition; definition of consensus and the threshold for achieving consensus. Items least addressed were: public patient involvement (PPI); the role of the steering committee, chair, cochair; conflict of interest of panellists and funding. Data extracted from included studies revealed additional items that were not captured in the data extraction form such as justification of deviation from the protocol or incentives to encourage panellist response., Conclusion: The results of this systematic review confirmed the need for a reporting checklist for consensus methodology and provided a range of potential checklist items to report. The next step in the ACCORD project builds on this systematic review and focuses on reaching consensus on these items to develop the reporting guideline., Protocol Registration: https://osf.io/2rzm9., Competing Interests: Competing interests: PL is a member of the UK EQUATOR Centre, an organisation that promotes the use of reporting guidelines, many of which are developed using consensus methods, and she is personally involved in the development of other reporting guidelines. ELH has worked with Ogilvy Health UK on consensus projects. WTG is a former employee of Ipsen and is now employed by Bristol Myers Squib. AP is an editor at the BMJ and is a senior research scientist at Stanford University, where she is responsible for advising and seeking funding on Delphi and other research studies. EJvZ and ZF have no conflict of interest., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Introduction: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques., Methods and Analysis: TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation., Ethics and Dissemination: Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications., Prospero Registration Number: CRD42019140361 and CRD42019161764., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Competing Interests: Competing interest: All authors completed an ICMJE conflicts of interest form, available upon request from the corresponding author. All authors are involved, as part of the EQUATOR Network, in the development, update, implementation and dissemination of reporting guidelines.

Competing Interests: Gary Collins is involved in the TRIPOD Statement.

Context and Objective: The success of vaccination campaigns depends on the degree of adherence to immunization initiatives and schedules. Risk factors associated with children's failure to receive the measles vaccine at the correct age were studied in the city of São Paulo, Brazil., Design and Setting: Case-control and exploratory study, in the metropolitan area of São Paulo., Methods: The caregivers of 122 children were interviewed regarding their perceptions and understanding about the measles vaccination and the disease., Results: The results showed that age, region of residence, marital status and education level were unrelated to taking measles vaccines adequately. Most individuals remembered being informed about the last annual vaccination campaign by television, but no communication channel was significantly associated with vaccination status. The answers to questions about knowledge of the disease or the vaccine, when analyzed alone, were not associated with taking measles vaccinations at the time indicated by health agencies. The results showed that, when parents felt sorry for their children who were going to receive shots, they delayed the vaccination. Most of the children did not take the measles vaccination on the exactly recommended date, but delayed or anticipated the shots., Conclusion: It is clear that there is no compliance with the government's recommended measles vaccination schedule (i.e. first dose at nine and second at 15 months of age, as recommended in 1999 and 2000). Feeling sorry for the children receiving shots can delay vaccination taking.